Thesis advisor: Arthur Lewbel / The thesis includes two essays. The first essay, Inequality Moments in Estimation of Discrete Games with Incomplete Information and Multiple Equilibria, develops a method for estimation of static discrete games with incomplete information, which delivers consistent estimates of parameters even when games have multiple equilibria. Every Bayes-Nash equilibrium in a discrete game of incomplete information is associated with a set of choice probabilities. I use maximum and minimum equilibrium choice probabilities as upper and lower bounds on empirical choice probabilities to construct moment inequalities. In general, estimation with moment inequalities results in partial identification. I show that point identification is achievable if the payoffs are functions of a sufficient number of explanatory variables with a real line domain and outcome-specific coefficients associated with them. The second essay, Tenancy Rent Control and Credible Commitment in Maintenance, co-authored with Richard Arnott, investigates the effect of tenancy rent control on maintenance and welfare. Under tenancy rent control, rents are regulated within a tenancy but not between tenancies. The essay analyzes the effects of tenancy rent control on housing quality, maintenance, and rehabilitation. Since the discounted revenue received over a fixed-duration tenancy depends only on the starting rent, intuitively the landlord has an incentive to spruce up the unit between tenancies in order to show it well, but little incentive to maintain the unit well during the tenancy. The essay formalizes this intuition, and presents numerical examples illustrating the efficiency loss from this effect. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

<div>A central issue in systems engineering is to design systems where the stakeholders do not behave as expected by the systems designer. Usually, these stakeholders have different and often conflicting objectives. The stakeholders try to maximize their individual objective and the overall system do not function as expected by the systems designers.</div><div><br></div><div><div>We specifically study two such systems- a) cloud-based design and manufacturing system (CBDM) and b) Air Transportation System (ATS). In CBDM, two stakeholders</div><div>with conflicting objectives are designers trying to get their parts printed at the lowest possible price and manufacturers trying to sell their excess resource capacity at maximum prots. In ATS, on one hand, airlines make route selection decision with the goal of maximizing their market share and prots and on the other hand regulatory bodies such as Federal Aviation Administration tries to form policies that increase overall welfare of the people.</div></div><div><br></div><div><div>The objective in this dissertation is to establish a mechanism design based framework: a) for resource allocation in CBDM, and b) to guide the policymakers in channeling the evolution of network topology of ATS.</div></div><div><br></div><div><div>This is the rst attempt in literature to formulate the resource allocation in CBDM as a bipartite matching problem with designers and manufacturers forming two distinct set of agents. We recommend best mechanisms in different CBDM scenarios like totally decentralized scenario, organizational scenario etc. based on how well the properties of the mechanism meet the requirements of that scenario. In addition to analyzing existing mechanisms, CBDM offers challenges that are not addressed in the literature. One such challenge is how often should the matching mechanism be implemented when agents interact over a long period of time. We answer this question through theoretical propositions backed up by simulation studies. We conclude that a matching period equal to the ratio of the number of service providers to the arrival rate of designers is optimal when service rate is high and a matching period equal to</div><div>the ratio of mean printing time to mean service rate is optimal when service rate is low.</div></div><div><br></div><div><div>In ATS, we model the evolution of the network topology as the result of route selection decisions made by airlines under competition. Using data from historic decisions we use discrete games to model the preference parameters of airlines towards explanatory variables such as market demand and operating cost. Different from the existing literature, we use an airport presence based technique to estimate these parameters. This reduces the risk of over-tting and improves prediction accuracy. We conduct a forward simulation to study the effect of altering the explanatory variables on the Nash equilibrium strategies. Regulatory bodies could use these insights while forming policies.</div></div><div><br></div><div><div>The overall contribution in this research is a mechanism design framework to design complex engineered systems such as CBDM and ATS. Specically, in CBDM a matching mechanism based resource allocation framework is established and matching mechanisms are recommended for various CBDM scenarios. Through theoretical and</div><div>simulation studies we propose the frequency at which matching mechanisms should be implemented in CBDM. Though these results are established for CBDM, these</div><div>are general enough to be applied anywhere matching mechanisms are implemented multiple times. In ATS, we propose an airport presence based approach to estimate</div><div>the parameters that quantify the preference of airlines towards explanatory variables.</div></div>

<p>This dissertation aims to introduce a new sequential Monte Carlo (SMC) based estimation framework for structural models used in macroeconomics and industrial organization. Current Markov chain Monte Carlo (MCMC) estimation methods for structural models suffer from slow Markov chain convergence, which means parameter and state spaces of interest might not be properly explored unless huge numbers of samples are simulated. This could lead to insurmountable computational burdens for the estimation of those structural models that are expensive to solve. In contrast, SMC methods rely on the principle of sequential importance sampling to jointly evolve simulated particles, thus bypassing the dependence on Markov chain convergence altogether. This dissertation will explore the feasibility and the potential benefits to estimating structural models using SMC based methods.</p><p> Chapter 1 casts the structural estimation problem in the form of inference of hidden Markov models and demonstrates with a simple growth model.</p><p> Chapter 2 presents the key ingredients, both conceptual and theoretical, to successful SMC parameter estimation strategies in the context of structural economic models.</p><p> Chapter 3, based on Chen, Petralia and Lopes (2010), develops SMC estimation methods for dynamic stochastic general equilibrium (DSGE) models. SMC algorithms allow a simultaneous filtering of time-varying state vectors and estimation of fixed parameters. We first establish empirical feasibility of the full SMC approach by comparing estimation results from both MCMC batch estimation and SMC on-line estimation on a simple neoclassical growth model. We then estimate a large scale DSGE model for the Euro area developed in Smets and Wouters (2003) with a full SMC approach, and revisit the on-going debate between the merits of reduced form and structural models in the macroeconomics context by performing sequential model assessment between the DSGE model and various VAR/BVAR models.</p><p> Chapter 4 proposes an SMC estimation procedure and show that it readily applies to the estimation of dynamic discrete games with serially correlated endogenous state variables. I apply this estimation procedure to a dynamic oligopolistic game of entry using data from the generic pharmaceutical industry and demonstrate that the proposed SMC method can potentially better explore the parameter posterior space while being more computationally efficient than MCMC estimation. In addition, I show how the unobserved endogenous cost paths could be recovered using particle smoothing, both with and without parameter uncertainty. Parameter estimates obtained using this SMC based method largely concur with earlier findings that spillover effect from market entry is significant and plays an important role in the generic drug industry, but that it might not be as high as previously thought when full model uncertainty is taken into account during estimation.</p> / Dissertation

Diese Dissertation präsentiert Modelle zur Lösung von Verhandlungsproblemen mit diskreten Strukturen. Hauptgrundlage der Betrachtung ist dabei die Erweiterung und Anwendung von Theorien der kooperativen Spieltheorie. Insbesondere der Shapley-Wert spielt eine wichtige Rolle. Als erste Problemklasse werden kooperative Spiele präsentiert, bei denen einige Spiele feste Auszahlungen erhalten. Als zweite Problemklasse werden kooperative Spiele untersucht, deren Lösungen ausschließlich ganzzahlig sein dürfen.

Diese Dissertation präsentiert Modelle zur Lösung von Verhandlungsproblemen mit diskreten Strukturen. Hauptgrundlage der Betrachtung ist dabei die Erweiterung und Anwendung von Theorien der kooperativen Spieltheorie. Insbesondere der Shapley-Wert spielt eine wichtige Rolle. Als erste Problemklasse werden kooperative Spiele präsentiert, bei denen einige Spiele feste Auszahlungen erhalten. Als zweite Problemklasse werden kooperative Spiele untersucht, deren Lösungen ausschließlich ganzzahlig sein dürfen.

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